Geoscience Reference
In-Depth Information
The signal measured at TOA is influenced by atmospheric, oceanic, and coupled
atmosphere-ocean effects. Within the VIS/NIR region of the electromagnetic spec-
trum, the water signal is only about 10% of the total TOA signal. About 90% of the
signal thus originates from atmospheric processes, such as scattering by aerosols.
For water quality monitoring, the atmospheric contribution of the detected signal
is unwanted and needs to be removed. This process is referred to as atmospheric
correction (Fischer and Fell 2001 , Schroeder et al. 2007a ) . Accurate atmospheric
correction is critical for the correct retrieval of water quality products.
Areas close to the coast are usually influenced by high reflectance from land.
Satellite data from water areas close to the coastline have to be corrected for these
so-called adjacency or environmental effects. Prototype algorithms to correct for
adjacency effects have been developed lately, e.g. the Improved Contrast between
Ocean and Land (ICOL) processor for MERIS data (Santer et al. 2007 ) .
20.2.1.5 Baltic Sea Remote Sensing
In order to retrieve the in-water constituent concentrations in the Baltic Sea from vis-
ible, spectral remote sensing data, a number of algorithms can be applied. Darecki
et al. ( 2003 ) suggested using reflectance ratios between spectral bands at 550 and
590 nm to derive chlorophyll- a concentration. The algorithm showed robust results
for Baltic Sea regions and is only little influenced by seasonal variations in CDOM.
Siegel and Gerth ( 2008 ) give a comprehensive overview on ocean remote sens-
ing algorithms in the Baltic Sea. Yet, no regional ocean colour algorithm has been
developed that is valid for the whole Baltic Sea basin. For the retrieval of the three
independently varying in-water constituents, complex approaches are needed, tak-
ing into account the full measured TOA spectrum (Doerffer and Schiller 2006a ,
Schroeder et al. 2007b ) . A number of coastal processors based on multispectral
regression techniques are available for MERIS data processing. Besides the official
ESA MERIS ground segment (IPF, i.e. the MERIS standard algorithm), four further
processors can be used in the Baltic area. For fresh water lakes, the Boreal Lakes
Water Processor and the Eutrophic Lakes Water Processor are available (Doerffer
and Schiller 2008a , b ) . For the Baltic Sea, the FUB/WeW Water Processor and the
Coastal Case-2 Regional Water Processor (C2R) can be applied (Schroeder et al.
2007a , b , Doerffer and Schiller 2006b , 2008a ) . The algorithms are based on neural
network inversion techniques to derive a number of bio-optical parameters simulta-
neously. Most of the in situ data used for the development of these processors are
from the North Sea or other European seas. These data do not represent the high
background CDOM absorption of 0.4/m -1 typical for the open Baltic Sea (Kratzer
and Tett 2009 ) , and level 2 products are underestimated in Baltic Sea waters, espe-
cially CDOM (Ohde et al. 2007 , Kratzer et al . 2008 , Vinterhav 2008 ) . More work
is needed on the atmospheric correction process over this optically complex water
(Sørensen et al. 2007 , Moore and Lavender 2010 ) . Furthermore, the ICOL processor
for correction of adjacency effects is also currently being improved. Another impor-
tant issue is the estimation of cyanobacterial biomass in relation to the biomass of
other phytoplankton species (Kutser et al. 2006 , Reinart and Kutser 2006 ) .
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